The goal of this proposal is to develop and validate novel compressed sensing (CS) approaches to dramatically improve the spatial and temporal resolution of quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). CS exploits prior information (assumptions) about MR images to infer missing data and produce high-quality images from significantly less data than previously thought possible. CS has already proven extremely successful in MR angiography and cardiac MRI, where it has accelerated some acquisitions by up to 10- to 100-fold, but is relatively unexplored in cancer imaging. DCE-MRI involves the serial acquisition of heavily T1-weighted images before and after the injection of a contrast agent to increase water relaxation rates in tissues. The resultin data can then be analyzed with appropriate pharmacokinetic models to extract quantitative parameters reporting on, for example, vessel perfusion and permeability, and tissue volume fractions. DCE-MRI has been applied to predict the early response to neoadjuvant chemotherapy in breast cancer, but the technique is not yet robust and accurate enough for the clinic. A fundamental practical limitation of DCE-MRI is the necessity to simultaneously acquire high temporal resolution, to adequately sample the contrast time course, and high spatial resolution, which is required for clinical morphological assessment and accurate tumor delineation. In traditional Cartesian MRI acquisitions, one must choose between high spatial or high temporal resolution before the scan. With a golden ratio acquisition, the tradeoff between spatial and temporal resolution is eliminated. A single DCE-MRI scan may then be used for both accurate kinetic modeling by slicing the data at high temporal cadence, while also allowing a high spatial resolution image to be formed by taking the data as a whole. Thus, a golden ratio acquisition coupled with CS has great potential to enable a clinically relevant DCE-MRI protocol that provides adequate temporal resolution for kinetic modeling without sacrificing the spatial resolution required for morphological evaluation. This project has three aims: (1) to develop a compressed sensing based high temporal resolution protocol for quantitative DCE-MRI, (2) to develop a compressed sensing based high spatial resolution T1-weighted anatomical imaging protocol for morphological evaluation, and (3) to apply the developed CS-based protocols in vivo for validation and evaluation. If this project is successful, it will significantly improve te ability to predict response to neoadjuvant chemotherapy, provide new CS methods for the community to apply to other in vivo applications, and validate CS in an important cancer imaging application.